code: def __init__(
      self,
      ctx: Context,
      model: Module,
      training_args: TrainingArguments,
      fed_args: FedArguments,
      train_set: Dataset,
      val_set: Dataset = None,
      loss_fn: Module = None,
      optimizer: Optimizer = None,
      scheduler: _LRScheduler = None,
      callbacks: List[TrainerCallback] = [],
      data_collator: Callable = None,
      tokenizer: Optional[PreTrainedTokenizer] = None,
      use_hf_default_behavior: bool = False,
      compute_metrics: Callable = None,
      local_mode: bool = False,
  ):
      super().__init__(
          ctx,
          model,
          training_args,
          fed_args,
          train_set,
          val_set,
          loss_fn,
          optimizer,
          data_collator,
          scheduler,
          tokenizer,
          callbacks,
          use_hf_default_behavior,
          compute_metrics=compute_metrics,
          local_mode=local_mode,
      )
  
imports: from fate.ml.nn.trainer.trainer_base import HomoTrainerClient, HomoTrainerServer
  from fate.ml.nn.trainer.trainer_base import FedArguments, TrainingArguments
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Callable, Union
  from fate.arch import Context
  from torch.optim import Optimizer
  from torch.utils.data import Dataset
  from torch.optim.lr_scheduler import _LRScheduler
  from transformers.trainer_callback import TrainerCallback
  from torch.nn import Module
  from torch import nn
  from torch.utils.data import DataLoader
  from transformers import TrainerState, TrainerControl, PreTrainedTokenizer
  from fate.ml.aggregator import AggregatorClientWrapper, AggregatorServerWrapper
  import logging
label_desc: Use image node_id 1 to create a new FedAVGClient object from inherited base classes: HomoTrainerClient with example: obj = FedAVGClient(ctx, model, training_args, fed_args, train_set, val_set, loss_fn, optimizer, scheduler, callbacks, data_collator, tokenizer, use_hf_default_behavior, compute_metrics, local_mode)